At-bit estimation of rock density from real-time drilling data using deep learning with online calibration

We present a novel streaming learning approach, utilizing a deep neural network (DNN) to learn from data available during operation to estimate at-bit density using drilling parameters. Since every wellbore is different, the relationship between drilling parameters and at-bit density varies. Equipment used, well trajectory, friction and bit wear are examples of conditions that affect this relationship and makes a pre-trained model unable to represent an accurate input/output mapping applicable to all wells. However, using delayed density log measurements, continuously supervising updates to the model is possible during operation. The algorithm has been tested on drilling data from wells on a field operated by Equinor and compared to a standard deep learning approach, where results show that a streaming learning approach outperforms the traditional method. Statistical analyses have been performed to verify the statistical significance and effect size on the data sets. Data visualizations using a t-distributed Stochastic Neighbor Embedding (t-SNE) indicate that the relationship between drilling parameters and density log indeed vary between wellbores, making generalizability an issue for a traditional supervised learning approach to this problem, and motivating a streaming learning approach. Using the proposed method, more accurate at-bit estimates can be made, providing preliminary indications ahead of the tool placed 20-30 m behind the bit, which, dependent on rate of penetration (ROP), will be available 20-120 min later.

Automated summary

A novel streaming learning approach is proposed, utilizing a deep neural network to estimate at-bit density using drilling parameters and continuously updating the model during operation, outperforming a standard deep learning approach. Statistical analyses and data visualizations confirm the variations in the relationship between drilling parameters and density log among different wellbores, highlighting the need for a streaming learning approach.